Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput Under Random Variables
Analysing and modelling efforts on production throughput are getting more complex due to random variables in today’s dynamic production systems. The objective of this study is to take multiple random variables of production into account when aiming for production throughput with higher accuracy of...
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| Format: | Article |
| Language: | English |
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Faculty Mechanical Engineering, UMP
2014
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| Online Access: | http://umpir.ump.edu.my/id/eprint/8238/ http://umpir.ump.edu.my/id/eprint/8238/ http://umpir.ump.edu.my/id/eprint/8238/ http://umpir.ump.edu.my/id/eprint/8238/1/Integration_of_Seasonal_Autoregressive_Integrated_Moving_Average_and_Bayesian_Methods_to_Predict_Production_Throughput_Under_Random_Variables.pdf |
Internet
http://umpir.ump.edu.my/id/eprint/8238/http://umpir.ump.edu.my/id/eprint/8238/
http://umpir.ump.edu.my/id/eprint/8238/
http://umpir.ump.edu.my/id/eprint/8238/1/Integration_of_Seasonal_Autoregressive_Integrated_Moving_Average_and_Bayesian_Methods_to_Predict_Production_Throughput_Under_Random_Variables.pdf